class_docstr: Sampling-based super-net training but using an RL agent to control the sampling.
  
  The default implementation for :class:`~nni.nas.strategy.ENAS`.
  
  See Also
  --------
  nni.nas.strategy.ENAS
  RandomSamplingLightningModule
docstr: Export the probability from ENAS controller directly.
docstr_tok: ["Export","the","probability","from","ENAS","controller","directly","."]
code: def export_probs(self) -> Sample:
      """Export the probability from ENAS controller directly."""
      self.policy.eval()
      if self.generator.sample_logits is None:
          with torch.no_grad():
              self.generator.next_sample()
      assert self.generator.sample_logits is not None
  
      # Convert {'a': [0.2, 0.5, 0.3]} to {'a': {'choice0': 0.2, 'choice1': 0.5, 'choice2': 0.3} }
      return {
          label: dict(
              zip(self.generator.simplified_space[label].values, logits)
          )
          for label, logits in self.generator.sample_logits.items()
      }
  
imports: from __future__ import annotations
  import warnings
  import logging
  from typing import Any, Callable, TYPE_CHECKING
  import pytorch_lightning
  import torch
  from nni.mutable import Sample
  from nni.nas.nn.pytorch import ModelSpace
  from .base_lightning import BaseOneShotLightningModule
  from .profiler import ProfilerPenalty, ExpectationProfilerPenalty, SampleProfilerPenalty
label_desc: Use image node_id 7 for calling the EnasLightningModule obj's underlying member method code with example usage: obj.export_probs() and returns: unknown